In-situ monitoring of image texturing via random forests and clustering with applications to additive manufacturing

IF 2 3区 工程技术 Q3 ENGINEERING, INDUSTRIAL IISE Transactions Pub Date : 2023-09-11 DOI:10.1080/24725854.2023.2257255
Fabio Caltanissetta, Luisa Bertoli, Bianca Maria Colosimo
{"title":"In-situ monitoring of image texturing via random forests and clustering with applications to additive manufacturing","authors":"Fabio Caltanissetta, Luisa Bertoli, Bianca Maria Colosimo","doi":"10.1080/24725854.2023.2257255","DOIUrl":null,"url":null,"abstract":"AbstractThe amount of attention paid to in-situ monitoring in Additive Manufacturing (AM) has significantly increased over the last few years, paving the way to a paradigm shift for quality monitoring and control via big data analysis of signals, images, and videos. In-situ quality monitoring represents an opportunity for waste reduction and first-time-right production via inline detection of process flaws, which allows early identification of scraps and the possibility of correcting actions for first-time-right production. This article presents a solution for in-situ monitoring of images taken layerwise in material extrusion AM. Compared with the existing solutions, mainly focusing on monitoring the shape deviation observed at each layer with respect to the nominal shape, this article focuses on monitoring the internal surface texture with the aim of detecting over- and under-extrusion flaws. Inspired by an approach reported in the literature that was developed for textile image monitoring, we propose a solution for in-situ monitoring of textured surfaces which is based on combining Random Forests with clustering to automatically identify defective locations layerwise. A real case study based on Fused Filament Fabrication is used to compare the performance of the novel proposed solution with the original one and identify an appropriate direction for future research.Keywords: Statistical quality monitoringin-situ monitoringimagerandom forestsclusteringadditive manufacturing Data availabilityThe data that support the findings of this study are openly available in figshare at https://doi.org/10.6084/m9.figshare.24042891.v1.Additional informationFundingThe present research was partially funded by ACCORDO Quadro ASI-POLIMI “Attività di Ricerca e Innovazione” n. 2018-5-HH.0, collaboration agreement between the Italian Space Agency and Politecnico di Milano.Notes on contributorsFabio CaltanissettaFabio Caltanissetta received his doctoral degree in industrial engineering from Politecnico di Milano (while completing this research work), after completing an MSc in industrial engineering at the same university. He is currently a Process R&D Specialist at Caracol AM.Luisa BertoliLaura Bertoli completed a Master of Science in industrial engineering at Politecnico di Milano, Italy (while completing this research work). She is currently a business data product specialist at UniCredit.Bianca Maria ColosimoBianca Maria Colosimo is a professor in the Department of Mechanical Engineering of Politecnico di Milano. Her research interest is mainly in the area of big data mining for Industry 4.0, with special focus on advanced manufacturing. She is currently a department editor of IISE Transactions, senior editor of Informs Journal of Data Science, associate editor of Progress in Additive Manufacturing and Additive Manufacturing Letters. She has been editor-in-chief of the Journal of Quality Technology (2018-2021). She is included among the top 100 Italian woman scientists in STEM","PeriodicalId":56039,"journal":{"name":"IISE Transactions","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IISE Transactions","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/24725854.2023.2257255","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0

Abstract

AbstractThe amount of attention paid to in-situ monitoring in Additive Manufacturing (AM) has significantly increased over the last few years, paving the way to a paradigm shift for quality monitoring and control via big data analysis of signals, images, and videos. In-situ quality monitoring represents an opportunity for waste reduction and first-time-right production via inline detection of process flaws, which allows early identification of scraps and the possibility of correcting actions for first-time-right production. This article presents a solution for in-situ monitoring of images taken layerwise in material extrusion AM. Compared with the existing solutions, mainly focusing on monitoring the shape deviation observed at each layer with respect to the nominal shape, this article focuses on monitoring the internal surface texture with the aim of detecting over- and under-extrusion flaws. Inspired by an approach reported in the literature that was developed for textile image monitoring, we propose a solution for in-situ monitoring of textured surfaces which is based on combining Random Forests with clustering to automatically identify defective locations layerwise. A real case study based on Fused Filament Fabrication is used to compare the performance of the novel proposed solution with the original one and identify an appropriate direction for future research.Keywords: Statistical quality monitoringin-situ monitoringimagerandom forestsclusteringadditive manufacturing Data availabilityThe data that support the findings of this study are openly available in figshare at https://doi.org/10.6084/m9.figshare.24042891.v1.Additional informationFundingThe present research was partially funded by ACCORDO Quadro ASI-POLIMI “Attività di Ricerca e Innovazione” n. 2018-5-HH.0, collaboration agreement between the Italian Space Agency and Politecnico di Milano.Notes on contributorsFabio CaltanissettaFabio Caltanissetta received his doctoral degree in industrial engineering from Politecnico di Milano (while completing this research work), after completing an MSc in industrial engineering at the same university. He is currently a Process R&D Specialist at Caracol AM.Luisa BertoliLaura Bertoli completed a Master of Science in industrial engineering at Politecnico di Milano, Italy (while completing this research work). She is currently a business data product specialist at UniCredit.Bianca Maria ColosimoBianca Maria Colosimo is a professor in the Department of Mechanical Engineering of Politecnico di Milano. Her research interest is mainly in the area of big data mining for Industry 4.0, with special focus on advanced manufacturing. She is currently a department editor of IISE Transactions, senior editor of Informs Journal of Data Science, associate editor of Progress in Additive Manufacturing and Additive Manufacturing Letters. She has been editor-in-chief of the Journal of Quality Technology (2018-2021). She is included among the top 100 Italian woman scientists in STEM
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于随机森林和聚类的图像纹理原位监测及其在增材制造中的应用
摘要在过去几年中,对增材制造(AM)现场监测的关注程度显著增加,为通过信号、图像和视频的大数据分析进行质量监测和控制的范式转变铺平了道路。通过在线检测工艺缺陷,现场质量监测为减少浪费和第一次正确生产提供了机会,这可以早期识别废料,并为第一次正确生产提供纠正措施的可能性。本文提出了一种材料挤压增材制造分层图像的现场监测方案。与现有的解决方案主要关注于监测每层观察到的形状相对于标称形状的偏差相比,本文主要关注于监测内部表面纹理,目的是检测过度和欠挤压缺陷。受文献报道的纺织品图像监测方法的启发,我们提出了一种基于随机森林和聚类相结合的纹理表面原位监测解决方案,以分层自动识别缺陷位置。通过一个基于熔丝制造的实际案例研究,比较了新提出的解决方案与原始解决方案的性能,并确定了未来研究的合适方向。关键词:统计质量监测原位监测图像随机森林聚类增材制造数据可用性支持本研究结果的数据可公开获取,共享网址:https://doi.org/10.6084/m9.figshare.24042891.v1.Additional information资助本研究部分由ACCORDO Quadro ASI-POLIMI“atitivitondi Ricerca e Innovazione”资助,2018-5-HH。意大利航天局与米兰理工大学之间的合作协议。fabio Caltanissetta在米兰理工大学获得工业工程硕士学位后,获得了工业工程博士学位(同时完成了这项研究工作)。他目前是Caracol AM的工艺研发专家。Luisa BertoliLaura Bertoli在意大利米兰理工大学(Politecnico di Milano)获得工业工程硕士学位(同时完成了这项研究工作)。她目前是UniCredit的商业数据产品专家。Bianca Maria Colosimo是米兰理工大学机械工程系的教授。主要研究方向为面向工业4.0的大数据挖掘,重点关注先进制造业。她目前是IISE Transactions的部门编辑,Informs Journal of Data Science的高级编辑,《增材制造进展》和《增材制造快报》的副主编。她曾担任Journal of Quality Technology(2018-2021)主编。她被列入意大利STEM领域前100名女科学家之一
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
IISE Transactions
IISE Transactions Engineering-Industrial and Manufacturing Engineering
CiteScore
5.70
自引率
7.70%
发文量
93
期刊介绍: IISE Transactions is currently abstracted/indexed in the following services: CSA/ASCE Civil Engineering Abstracts; CSA-Computer & Information Systems Abstracts; CSA-Corrosion Abstracts; CSA-Electronics & Communications Abstracts; CSA-Engineered Materials Abstracts; CSA-Materials Research Database with METADEX; CSA-Mechanical & Transportation Engineering Abstracts; CSA-Solid State & Superconductivity Abstracts; INSPEC Information Services and Science Citation Index. Institute of Industrial and Systems Engineers and our publisher Taylor & Francis make every effort to ensure the accuracy of all the information (the "Content") contained in our publications. However, Institute of Industrial and Systems Engineers and our publisher Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Institute of Industrial and Systems Engineers and our publisher Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Institute of Industrial and Systems Engineers and our publisher Taylor & Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to, or arising out of the use of the Content. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions .
期刊最新文献
MFRL-BI: Design of a Model-free Reinforcement Learning Process Control Scheme by Using Bayesian Inference Design of unreliable flow lines: How to jointly allocate buffer space and spare parts Robust Optimization Approaches in Inventory Management: Part A - The Survey Robust Optimization Approaches in Inventory Management: Part B - The Comparative Study FUSION3D: Multimodal Data Fusion for 3D Shape Reconstruction - A Soft Sensing Approach
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1